Laguna by Poolside vs LMArena: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and LMArena — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.
LMArena
LMArena
Open platform for crowdsourced benchmarking and live leaderboards that ranks chatbots and LLMs using user votes and automated evaluations.
Key features
- Crowdsourced Pairwise Voting: Users can interact with multiple chatbots and cast pairwise votes; aggregated human preferences are used to compute model win-rates and power the live leaderboard.
- Bradley–Terry Ranking Engine: Uses the Bradley–Terry statistical model to convert pairwise user votes into continuous rankings and win-rate metrics for robust comparison between models.
- Arena-Hard-Auto Evaluation Suite: Provides an automated benchmark (Arena-Hard-Auto) with curated hard prompts, style-control features, and the ability to use GPT-4.1/Gemini judges for pre-deployment model assessment.
- Public Datasets and Preference Collections: Hosts multiple datasets (e.g., search-arena-24k, arena-human-preference-140k) and preference data on Hugging Face for training, evaluation, and replication of leaderboard results.
- Hugging Face Spaces & Model Repos: Maintains interactive leaderboards and example apps as Hugging Face Spaces and publishes model and dataset repositories for community use and reproducibility.
- FastChat Integration for Serving: Commonly integrated with FastChat to serve and evaluate chatbots in live comparisons and crowdsourced matches, enabling scalable interactive evaluations.
- Open Tooling & Scripts: Provides open-source scripts and configuration (e.g., config YAMLs, result display scripts) to run evaluations, add style attributes, and compute win rates under different judge configurations.
- Crowdsourced pairwise voting system driving live leaderboards (Bradley-Terry ranking)
- Public leaderboard and web chat interface (lmarena.ai) to try and compare models
- Arena-Hard-Auto: automated evaluation toolkit and benchmark with configurable judges (supports GPT-4.1/Gemini as judges)
- Integration with FastChat for training, serving, and evaluating chatbots
- Hugging Face presence: publishes datasets, benchmark suites, models, and Spaces (leaderboard Space)
- Open datasets for benchmarking (e.g., search-arena-24k, arena-hard datasets)
- Support for custom model evaluation via config YAML (model_list) and Python tooling (show_result.py, add_markdown_info.py)
- Model formats and training artifacts compatible with PyTorch/transformers (AutoTokenizer usage, model repo examples)
- Support for multi-modal evaluation and specialized arenas (e.g., VisionArena)
- Plugins/compatibility with external APIs (OpenAI API for GPT judges) and community model repos
Best for
- Pre-deployment Model Evaluation: Run Arena-Hard-Auto to estimate how a candidate model will perform on LMArena-style human preference comparisons before public release.
- Live Comparative Benchmarking: Publish a chatbot endpoint and compare it against other models on the live LMArena leaderboard to measure relative win rates from real user votes.
- Research on Human Preferences: Use the arena-human-preference datasets to study preference patterns, fine-tune models on preference data, or reproduce published leaderboard outcomes.
- Automated Stress Testing: Evaluate robustness and style-control behavior of models using Arena-Hard-Auto’s hard prompts and judge ensembles (GPT-4.1/Gemini) to surface failure modes.
- Dataset-driven Fine-tuning: Leverage LMArena-hosted datasets (search-arena-24k, others) to fine-tune conversational models for better performance on human-preference metrics.
- Community Benchmarking & Transparency: Host community challenges and transparent leaderboards via Hugging Face Spaces and GitHub repos to encourage reproducible, open comparisons.
- Evaluate and compare chatbot/LLM performance with real user votes and automated judges
- Pre-deployment validation: run Arena-Hard-Auto to estimate likely performance on the public leaderboard
- Publish research models, datasets, and leaderboards for community benchmarking and reproducibility
- Build and serve chatbots using FastChat integration and measure user preference on LMArena
- Run automated, configurable evaluations using ensemble judges (GPT-4.1, Gemini, etc.)